diffusers/main/en/api/models/wan_animate_transformer_3d.md

WanAnimateTransformer3DModel

A Diffusion Transformer model for 3D video-like data was introduced in Wan Animate by the Alibaba Wan Team.

The model can be loaded with the following code snippet.

from diffusers import WanAnimateTransformer3DModel

transformer = WanAnimateTransformer3DModel.from_pretrained("Wan-AI/Wan2.2-Animate-14B-Diffusers", subfolder="transformer", torch_dtype=torch.bfloat16)

WanAnimateTransformer3DModel[[diffusers.WanAnimateTransformer3DModel]]

• patch_size (tuple[int], defaults to (1, 2, 2)) -- 3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
• num_attention_heads (int, defaults to 40) -- Fixed length for text embeddings.
• attention_head_dim (int, defaults to 128) -- The number of channels in each head.
• in_channels (int, defaults to 16) -- The number of channels in the input.
• out_channels (int, defaults to 16) -- The number of channels in the output.
• text_dim (int, defaults to 512) -- Input dimension for text embeddings.
• freq_dim (int, defaults to 256) -- Dimension for sinusoidal time embeddings.
• ffn_dim (int, defaults to 13824) -- Intermediate dimension in feed-forward network.
• num_layers (int, defaults to 40) -- The number of layers of transformer blocks to use.
• window_size (tuple[int], defaults to (-1, -1)) -- Window size for local attention (-1 indicates global attention).
• cross_attn_norm (bool, defaults to True) -- Enable cross-attention normalization.
• qk_norm (bool, defaults to True) -- Enable query/key normalization.
• eps (float, defaults to 1e-6) -- Epsilon value for normalization layers.
• image_dim (int, optional, defaults to 1280) -- The number of channels to use for the image embedding. If None, no projection is used.
• added_kv_proj_dim (int, optional, defaults to 5120) -- The number of channels to use for the added key and value projections. If None, no projection is used.

A Transformer model for video-like data used in the WanAnimate model.

• hidden_states (torch.Tensor of shape (B, 2C + 4, T + 1, H, W)) -- Input noisy video latents of shape (B, 2C + 4, T + 1, H, W), where B is the batch size, C is the number of latent channels (16 for Wan VAE), T is the number of latent frames in an inference segment, H is the latent height, and W is the latent width.
• timestep -- (torch.LongTensor): The current timestep in the denoising loop.
• encoder_hidden_states (torch.Tensor) -- Text embeddings from the text encoder (umT5 for Wan Animate).
• encoder_hidden_states_image (torch.Tensor) -- CLIP visual features of the reference (character) image.
• pose_hidden_states (torch.Tensor of shape (B, C, T, H, W)) -- Pose video latents. TODO: description
• face_pixel_values (torch.Tensor of shape (B, C', S, H', W')) -- Face video in pixel space (not latent space). Typically C' = 3 and H' and W' are the height/width of the face video in pixels. Here S is the inference segment length, usually set to 77.
• motion_encode_batch_size (int, optional) -- The batch size for batched encoding of the face video via the motion encoder. Will default to self.config.motion_encoder_batch_size if not set.
• return_dict (bool, optional, defaults to True) -- Whether to return the output as a dict or tuple.
• attention_kwargs (dict, optional) -- A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under self.processor in diffusers.models.attention_processor.~models.transformer_2d.Transformer2DModelOutput or tupleIf return_dict is True, a ~models.transformer_2d.Transformer2DModelOutput whose sample is the denoised video latent is returned, otherwise a plain tuple whose first element is that tensor is returned.

Forward pass of Wan2.2-Animate transformer model.

Transformer2DModelOutput[[diffusers.models.modeling_outputs.Transformer2DModelOutput]]

• sample (torch.Tensor of shape (batch_size, num_channels, height, width) or (batch size, num_vector_embeds - 1, num_latent_pixels) if Transformer2DModel is discrete) -- The hidden states output conditioned on the encoder_hidden_states input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.